Intro

Concern over the detrimental consequences of traffic noise on mental health, especially the risk of depression, has grown in recent years. Millions of individuals worldwide are impacted by the widespread environmental stressor known as traffic noise, which has been shown to have negative impacts on physical health. Little is known about how traffic noise affects mental health, particularly depression. Exposure to excessive noise has been related to a number of physical and mental health issues, including cardiovascular disease, sleep disorders, and anxiety. Noise pollution is a severe health risk. The influence of traffic noise on mental health is a growing field of research since it contributes significantly to noise pollution in metropolitan settings. Several research have indicated a link between exposure to traffic noise and a higher risk of developing depression, although the underlying processes are still not entirely understood. While some experts contend that traffic noise may raise the likelihood of depression by interfering with sleep and elevating stress levels, others contend that it may have an impact on the brain’s ability to think clearly and regulate emotions. For successful methods to reduce the detrimental impacts of noise pollution on mental health, it is crucial to comprehend the link between traffic noise and depression. A deeper understanding of the connection between these two variables is essential for public health given the high incidence of traffic noise exposure in metropolitan settings and the large burden of depression. With a focus on the underlying processes and potential health effects, this study intends to examine the available research on the relationship between traffic noise and depression. The results of this study might help politicians and urban planners create practical plans to lessen the negative effects of traffic noise on mental health and raise standards of living generally in cities.

Litrature Review

Literature 0 - EPA Identifies Noise Levels Affecting Health and Welfare

In order to protect the public health and welfare from hearing loss, annoyance, and activity interference, the Environmental Protection Agency (EPA) has published a document titled “Information on Levels of Environmental Noise Requisite to Protect Public Health and Welfare with an Adequate Margin of Safety.” According to the paper, noise levels of 55 decibels outside and 45 decibels indoors will avoid activity interference and irritation, but exposure to 70 decibels of noise over a 24-hour period will prevent detectable hearing loss over a lifetime. These noise levels are averages of acoustic energy throughout time rather than peak values. Depending on the area’s usage, the EPA regulation specifies varied noise limits, with 45 dB being connected with indoor living places, hospitals, and schools. For outdoor locations where there is human activity, 55 decibels have been determined, and 70 decibels should be used everywhere to avoid hearing damage. State and local governments can utilize the information in the paper to establish noise limits, but other pertinent aspects including costs and benefits, local ambitions, and tools available to regulate environmental noise must also be taken into account.

Literature 1 – Study of physio-psychological effects on traffic wardens due to traffic noise pollution; exposure-effect relation

Pakistan is not an exception to the global problem of noise pollution in metropolitan areas. In fact, because to its immediate acute and long-term physio-psychological impacts, it is frequently seen as being more harmful than air and water pollution. The goal of this study was to examine and assess the psychological and physical consequences of traffic noise on traffic wardens in Taxila and Islamabad, two cities in Pakistan. The survey for this study was conducted at three checkpoints that were near to roads. The study comprises a month’s worth of noise measurements at the checkpoints and interviews with traffic wardens using a Performa-based questionnaire. The results showed that noise levels varied from 85 to 106 dB, which is prohibited by OSHA. The psychological symptoms seen in the wardens were aggravated melancholy (58%), stress (65%), public conflict (71%), impatience and irritation (54%), behavioral repercussions (59%), and speech interference (56%) among others. High blood pressure (87%), tense muscles (64%), weariness (48%), poor performance (55%), loss of attention (93%), hearing loss (69%), headache (74%), and cardiovascular issues (71%) were some of the physiological symptoms. A straightforward regression test was carried out in Excel to ascertain the link between exposure duration and the impacts of traffic noise on the wardens. As exposure duration increased, the percentage of psychological and physical symptoms in the wardens changed. In contrast to hypertension, which had an R2 value of 0.96 and a P value of 0.00095, exacerbated depression had an R2 value of 0.946 and a P value of 0.133. The fast urbanization, industrialization, and population growth in Pakistan have significantly altered the social and economic makeup of the country. Traffic loads are growing alarmingly quickly due to improvements in transportation infrastructure and technology, which leads to a serious issue with noise pollution. Unwanted sound that varies in frequency and acoustic pressure and lacks a predictable pattern is called noise. The main contributors to noise pollution are air, train, and road traffic. The characteristics of noise (frequency and acoustic pressure) depend on the characteristics of the traffic and the road, such as the road gradient, surface type, surrounding topography, grade of the road, the number and type of vehicles, the age of the vehicles passing, the speed of the vehicles, the type of goods transported, the packing of the goods in the vehicles, the sound pressure of the horn, the type of brakes, and the behavior of the drivers. Reducing noise pollution at the source, such as the car and road, is the most efficient approach to do it. The air intake system, exhaust system, tires, and engine all contribute differently to vehicle noise, with the air intake system contributing 9%, the exhaust system 27%, the tires 30%, and the engine 34%. At 70 km/h, tire noise predominates over other interior noise sources. As a result, lowering the speed at which cars travel on crowded highways significantly lowers the noise level. Just a 5% decrease in the road’s grade can lower the Leq noise by 1.5 dB. To cut down on noise, the road surface macro-texture wavelength range should be 2-10-20 mm. Similar to green belts, plants, and trees, they effectively lower noise levels around roadways. It is advised to build mounds and barriers alongside the highways to reduce noise by 10 dB. Building treatments, such as single- and double-glazed windows that lower noise levels by 10 and 25 dB, respectively, are also helpful in reducing internal noise. It’s crucial to control noise pollution to prevent any negative impacts. Excessive noise levels harm people’s health and cause financial losses. The fundamental tenet of human rights is the right to live in a clean environment. As a result, everyone is accountable.

Literature 2 - Noise Pollution-Sources, Effects and Control

The first section of the article discusses the several types of noise pollution, both natural and man-made. Thunderstorms, earthquakes, and animal sounds are examples of natural sources, whereas automobiles, trains, and aircraft, as well as industry and construction, as well as recreational noise, are examples of man-made sources (e.g. concerts, sporting events). After that, the writers go on to discuss how noise pollution affects people’s health and wellbeing. Many adverse health effects, such as hearing loss, sleep disturbance, cardiovascular disease, and mental health issues including anxiety and depression, can arise from exposure to excessive noise. Children, the elderly, and those with pre-existing medical disorders are among the categories who might be most negatively impacted by noise pollution. The essay also touches on the financial effects of noise pollution, such as lost productivity and medical expenses. The scientists also point out that noise pollution can harm biodiversity by obstructing animal communication and resulting in habitat loss. The article also covers methods for reducing noise pollution. They include actions like installing soundproofing, noise barriers, and using quieter technology. The authors stress the need of both individual and societal effort in combating noise pollution, stating that alterations in behavior and governmental regulations may both contribute to lower noise levels. Overall, the essay offers a thorough analysis of the causes, impacts, and measures to reduce noise pollution, emphasizing the need of tackling this ubiquitous and frequently disregarded environmental problem.

Literature 3 - Noise pollution: non-auditory effects on health

The non-auditory consequences of noise pollution on our health were explored in this paper by Stansfeld & Matheson in 2003. It begins by stating that a lifetime of exposure to noise levels of 85 to 90 dBA in industrial settings can cause a gradual loss of hearing. Environmental noise is present at far lower levels than industrial noise, and its impacts on non-auditory health cannot be attributed to sound energy. The important lesson from this essay, as with the other ones, is that irritation is the main effect of noise pollution, which can then cause stress reactions, physical symptoms, and even illness. Hence, noise pollution has an indirect effect on our health. Yet, noise may directly affect health rather than merely being a nuisance. The reaction to noise may vary depending on the noise’s qualities, such as its strength, frequency, complexity, duration, and meaning. Even after adjusting for sociodemographic characteristics and baseline psychiatric condition, the study revealed no connection between airplane noise and road traffic noise and psychiatric problem, while there was a little non-linear link between noise and higher anxiety ratings. Road traffic noise has been marginally related with mental health symptoms after correcting for age, sex, income, and duration of residence. Several research in Japan revealed an association between exposure to higher levels of military aircraft noise and depressiveness and anxiousness. In general, it appears that ambient noise is associated with psychological symptoms but not with clinical psychiatric disorders. At far greater noise levels, there could be a connection to psychiatric disease, though. The outcomes in the area of physical health were comparable and included rising blood pressure, cardiovascular conditions, and sleeplessness.

Literature 4 - Mental health effects of education

In the case of Zimbabwe, the research investigates the connection between education and mental health. The authors evaluate the causal impact of education on mental health in later life using an instrumental variable (IV) method. Age-specific exposure to an educational reform in Zimbabwe during the 1980s served as the study’s IV. Before 1980, Zimbabwe was a British colony, and there were a number of discriminatory laws and regulations that limited the educational opportunities available to Black Zimbabweans. After gaining independence, the new government put changes into place that helped Black children who were enrolled in elementary education at the time. Children in Zimbabwe began attending school at age six, therefore those who were 13 or younger in 1980 benefited disproportionately from these measures (treatment group). Children who were 14 or 15 years old in 1980 who were part of the partially treated group also made some academic progress. The control group is made up of older people (16 years or older at the time), who were much less likely to experience any such advantages. The treated group finally accrued about three years of schooling and had a 39% point higher likelihood of attending secondary school, according to the authors. According to the IV findings, this improved education may have contributed to greater mental health in later life. An additional year of education decreased the likelihood of reporting any symptoms of sadness (11.3%) or anxiety (9.8%) in adulthood and also decreased the intensity of both depression (6.1%) and anxiety (5.6%) symptoms. Women and those living in rural areas are more affected by education’s influence on mental health. The authors also discover data suggesting that increased female empowerment, better health-related behavior, and greater physical health may be some of the ways that education may have influenced mental health in the Zimbabwean environment. The results of this study add to two distinct literary streams. It first strengthens the mounting body of research demonstrating the connection between education and mental health. Although some studies have established a link between education and mental health, others have found contradictory findings. This study employs a distinctive setting and a rigorous empirical methodology to give strong evidence of the causal relationship between education and mental health. The report also emphasizes the significance of funding education in low-income nations as a way to enhance mental health outcomes. In low-income nations, mental health is stigmatized and undertreatment is common. Bettering educational performance can have a favorable impact on socioeconomic outcomes as well as mental health. The report has various consequences for policy. First of all, it emphasizes the necessity for governments to fund education as a way to enhance mental health outcomes. Second, it implies that measures to lessen gender and rural-urban differences in educational outcomes may have significant positive effects on mental health. Thirdly, it draws attention to the need to lessen the stigma attached to mental health problems in low-income nations, which deters some individuals from seeking help. Lastly, it offers proof that enhancing physical health and health-related behaviors can have a favorable knock-on effect on results in mental health. The report emphasizes the value of education spending overall as a strategy of enhancing mental health and other socioeconomic outcomes.

Literature 5 - Income inequality and depression: a systematic review and meta‐analysis of the association and a scoping review of mechanisms

The goal of the study was to investigate the relationship between wealth disparity and depression, one of the most common mental health diseases worldwide. With the purpose of identifying plausible processes behind this link, the researchers undertook a scoping study in addition to a systematic review and meta-analysis of the prior studies on depression and income disparity. The search term used for the systematic review was “(depress* OR mental) AND (inequal* OR Gini),” and it covered a number of databases including PubMed/Medline, EBSCO, and PsycINFO. The search was restricted to human subject studies that were published in English between January 1, 1990, and July 31, 2017. Also, any pertinent studies or search phrases were manually looked for in the reference lists of all included research. Studies presenting primary quantitative data with a measure of depression or depressed symptoms as an outcome as well as any measure of income disparity at any scale of geography met the inclusion criteria. Unpublished data, qualitative research, and publications reporting duplicate data from the same population were excluded from consideration. The researchers used the Systematic Assessment of Quality in Observational Research (SAQOR) technique to conduct quality assessments after reviewing the titles and abstracts and collecting full-text articles for pertinent studies. Six categories under the SAQOR include sample, exposure/outcome measures, confounders, and data reporting. With a pooled effect estimate of 1.16 (95% CI: 1.07-1.25) in the meta-analysis, the study’s findings demonstrated a statistically significant positive connection between income inequality and the prevalence of depression. The findings held true at a variety of geographic scales, including income disparity at the national, state/provincial, and local levels. A number of pathways, including social comparison and status anxiety, psychosocial stress, a lack of social support and cohesion, decreased social mobility, and increased exposure to environmental toxins and pollutants, were identified in the scoping review of potential mechanisms underlying the association between income inequality and depression. The authors put out a theoretical framework that incorporates these pathways and emphasizes the part played by social determinants of health and policies that affect how income and wealth are distributed in forming the relationship between income disparity and depression. The study has a number of ramifications for practice and policy. According to the findings, lowering economic disparity may benefit depression prevention and treatment, especially for vulnerable groups like women and those who are poor. The study also emphasizes how crucial it is to address socioeconomic determinants of health, such as housing, employment, and education, in order to improve mental health and lessen health inequalities. The authors urge more investigation into the causal processes behind the link between income inequality and depression as well as the development and evaluation of therapies that specifically address these mechanisms.

Literature 6 - Relationship Between Long Working Hours and Depression

This article explores the connection between clerical employees’ excessive hours and depressed symptoms. The article opens by emphasizing that major depressive disorder and depressed state are significant occupational health issues in industrialized nations, and several research have examined the association between various states of mental and physical health and high job demand and/or limited job control. The article then focuses on how lengthy work hours relate to different mental and physical health conditions, with numerous research indicating a detrimental impact. Few research have examined the long-term consequences, and the results have not always been consistent. The article presents current research that shows people who work long hours have a higher chance of developing new depression symptoms or severe depressive episodes than people who work 7 to 8 hours per day. The long-term implications of extended working hours are still little understood, though. In order to better understand how long working hours impact future depressed moods and explore how people who work long hours are at a higher risk of developing depression, this prospective study set out to answer these questions. Unlinkable anonymous data gathered through self-administered questionnaires was used to perform the study. The article contains information on the study demographics and research strategy, including a longitudinal study that used repeated assessments of variables at each of the four time points during the course of the three-year follow-up. According to the study, those who work long hours and are overworked have a higher chance of developing depressive disorders in the future. The implications of the findings for prospective measures to lower the likelihood of severe depression in employees are covered in the article’s conclusion.

Methodology

My 3 main data sets are:

  1. Noise Pollution - The Environmental Noise Directive, which was established in 2002, required all 27 of the member states of Europe to publish a noise report every five years for each city with a population of over 100,000 people. The reports will be broken down into 5 noise levels: 55 to 59 dB, 60 to 64 dB, 65 to 69 dB, 70 to 74 dB, and >75 dB. For each level of noise, the number of people exposed to it will be listed under it. The decibels are the average for the entire day (7:00–19:00); there is a 5 dB penalty for evening noise (19:00–23:00), and a 10 dB penalty for nighttime noise (23:00–7:00). Due to END’s requirement that only member states publish the report, much of my most recent data had NA values. After removing these values, I was left with cities from 30 different countries. The most recent data were from 2017 and included all cities in Europe with over 100,000 inhabitants from 36 different countries. The European Environment Agency website served as the source for this data and information.

2)Mental Health Disorders - The next set of data I required was that pertaining to mental health conditions, such as depression, anxiety disorders, bipolar disorder, eating disorders, and schizophrenia. These numbers, which represent the percentages of the population affected by each disorder, were taken from Our World in Data. Since I already had information on noise pollution at the city level, I decided to look for information on mental health there as well. However, this information was incredibly hard to come by and was only available at the national level. To resolve this discrepancy, I decided to add the number of people exposed to each noise level and group my noise pollution data by countries. This, however, had two major issues:

The percentage of the population exposed to various noise levels isn’t based on the entire population but rather only on people who live in cities with 100,000 or more residents, whereas the data on mental health is based on the entire population.

  1. The third and final set of data will include indices that are thought to be associated with rates of mental health disorders:

Mean years of schooling (MYS) is an index that measures the typical length of time that people in a population spend in school. It is a gauge of a society’s or a nation’s level of education. The total number of years of education completed by a group of people is divided by the total number of people in the group to determine the mean years of education. For instance, if a group of 100 people has completed 1,000 years of education in total, the mean number of years spent in school for that group is 10. The average number of years spent in school can be used to compare education levels between various groups, such as men and women or different regions of a country. It also serves as a starting point for a number of social and economic analyses, including the United Nations Development Program’s (UNDP) calculation of the Human Development Index (HDI) (UNDP).

The average number of years spent in school was gathered from “Global Data Lab.”

The GINI index, also known as the Gini coefficient, is a statistical indicator used to show how wealth or income is distributed among a population. The concept was created in 1912 by an Italian statistician by the name of Corrado Gini. The Gini index ranges from 0 to 1, with 0 signifying perfect equality (where everyone has the same level of wealth or income) and 1 signifying perfect inequality (where one person has all the income or wealth and everyone else has none). When determining the Gini index, The cumulative share of the population’s income or wealth is first plotted against the cumulative share of the population using the Lorenz curve. The area between the Lorenz curve and the line of perfect equality, which is the diagonal line running from the bottom left corner to the top right corner of the graph, is then divided by the total area beneath the line of perfect equality to determine the Gini index. The Gini index is frequently employed as a gauge of wealth or income disparity in a community or nation. More inequality is denoted by higher Gini coefficients, and greater equality is shown by lower Gini values. The index can be used to compare wealth or income distributions across time or between various nations.

Data for the GINI index were gathered from “The World Bank” website.

The final piece of information thought to be related to depression is the number of hours worked per year in each European nation in 2017. This information was taken from Our World in Data. According to Eurostat, the average number of hours worked annually by each employed individual in the EU-28 in 2017 was 1,569. This statistic can, however, differ considerably between nations and may be impacted by elements including labor laws, collective bargaining agreements, and cultural norms. It should be noted that the EU-28 at the time included the UK, which has since departed the EU, and referred to all 28 members of the European Union.

Limitations: It’s important to note that I started the data collecting process with 36 countries but ended up with 27 countries due to no data available at some of the countries, the countries that were removed from the research are:

Limitations: It’s crucial to note that I began the data collection process with 36 nations but ended up with 27 since several of the countries had no data accessible. The countries that were eliminated from the research are:

Austria, Belgium, Bulgaria, Croatia, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom were the nations that remained.

Appendices:

The percentages of the measured population exposed to 55+ dB are shown in this map:

The percentages of the people exposed to 55+ dB are shown on this map:

The percentages of the population that experience depression are shown on this map:

The percentages of the population that experience anxiety are shown on this map:

The percentages of the population with eating disorders are shown on this map:

The percentages of the population that have bipolar disorder are shown on this map:

The percentages of the people with schizophrenia are shown on this map:

The Gini index for the EU nations is shown on this map:

The mean number of years spent in education in each nation is shown on this map:

The average yearly working hours are shown on this map for each nation: